Package software.amazon.awscdk.services.bedrock.alpha

Interface BedrockFoundationModelProps

All Superinterfaces:
software.amazon.jsii.JsiiSerializable
All Known Implementing Classes:
BedrockFoundationModelProps.Jsii$Proxy
@Generated(value="jsii-pacmak/1.119.0 (build 1634eac)", date="2025-11-13T16:10:17.895Z") @Stability(Experimental) public interface BedrockFoundationModelProps extends software.amazon.jsii.JsiiSerializable
(experimental) Properties for configuring a Bedrock Foundation Model.

These properties define the model's capabilities and supported features.

Example: 

 // The code below shows an example of how to instantiate this type.
 // The values are placeholders you should change.
 import software.amazon.awscdk.services.bedrock.alpha.*;
 BedrockFoundationModelProps bedrockFoundationModelProps = BedrockFoundationModelProps.builder()
         .legacy(false)
         .optimizedForAgents(false)
         .supportedVectorType(List.of(VectorType.FLOATING_POINT))
         .supportsAgents(false)
         .supportsCrossRegion(false)
         .supportsKnowledgeBase(false)
         .vectorDimensions(123)
         .build();

  • Method Details

    • getLegacy

      @Stability(Experimental) @Nullable default Boolean getLegacy()
      (experimental) https://docs.aws.amazon.com/bedrock/latest/userguide/model-lifecycle.html A version is marked Legacy when there is a more recent version which provides superior performance. Amazon Bedrock sets an EOL date for Legacy versions.

      Default: - false

    • getOptimizedForAgents

      @Stability(Experimental) @Nullable default Boolean getOptimizedForAgents()
      (experimental) Currently, some of the offered models are optimized with prompts/parsers fine-tuned for integrating with the agents architecture.

      When true, the model has been specifically optimized for agent interactions.

      Default: - false

    • getSupportedVectorType

      @Stability(Experimental) @Nullable default List<VectorType> getSupportedVectorType()
      (experimental) Embeddings models have different supported vector types.

      Defines whether the model supports floating-point or binary vectors.

      Default: - undefined

    • getSupportsAgents

      @Stability(Experimental) @Nullable default Boolean getSupportsAgents()
      (experimental) Bedrock Agents can use this model.

      When true, the model can be used with Bedrock Agents for automated task execution.

      Default: - false

    • getSupportsCrossRegion

      @Stability(Experimental) @Nullable default Boolean getSupportsCrossRegion()
      (experimental) Can be used with a Cross-Region Inference Profile.

      When true, the model supports inference across different AWS regions.

      Default: - false

    • getSupportsKnowledgeBase

      @Stability(Experimental) @Nullable default Boolean getSupportsKnowledgeBase()
      (experimental) Bedrock Knowledge Base can use this model.

      When true, the model can be used for knowledge base operations.

      Default: - false

    • getVectorDimensions

      @Stability(Experimental) @Nullable default Number getVectorDimensions()
      (experimental) Embedding models have different vector dimensions.

      Only applicable for embedding models. Defines the dimensionality of the vector embeddings.

      Default: - undefined

    • builder

      @Stability(Experimental) static BedrockFoundationModelProps.Builder builder()
      Returns:
      a BedrockFoundationModelProps.Builder of BedrockFoundationModelProps